While public awareness of climate change has grown over the years, many people still have misconceptions regarding effective individual environmental action. In this paper, we present a serious game called PEAR, developed using elements of geolocation and augmented reality (AR), aimed at increasing players’ awareness of climate change issues and propensity for effective sustainable behaviours. We conducted a study with participants who played the game, gauging their knowledge of and attitudes towards climate change issues before and after playing the game. Our results show that the game significantly improved participants’ knowledge on sustainability and climate-change-related issues, and that it also significantly improved their attitudes towards these topics, thus proving that serious games have the potential to impart knowledge and promote sustainable behaviours. Additionally, our results address the lack of empirical studies on the knowledge base of serious sustainability games by introducing methods of quantitatively analysing the effects of serious sustainability games while additionally providing more knowledge about the effectiveness of the specific design elements of our game.

As the world rapidly urbanizes in pace with economic growth, the rising demand for products and services in cities is putting a strain on the existing road infrastructure, leading to traffic congestion and other negative externalities. To mitigate the impacts of freight movement within commercial areas, city planners have begun focusing their attention on the parking behaviors of commercial vehicles. Unfortunately, there is a general lack of information on such activities because of the heterogeneity of practices and the complex nature of urban goods movement. Furthermore, field surveys and observations of truck parking behavior are often faced with significant challenges, resulting in the collection of sparse and incomplete data. The objective of this study is to develop a regression model to predict the parking duration of commercial vehicles at the loading bays of retail malls and identify significant factors that contribute to this dwell time. The dataset used in this study originates from a truck parking and observation survey conducted at the loading bays of nine retail malls in Singapore, containing information about the trucks’ and drivers’ activities. However, because of the presence of incomplete fields found in the dataset, the authors propose the use of a generative adversarial multiple imputation networks algorithm to impute the incomplete fields before developing the regression model using the imputed dataset. Through the parking duration model, the activity type, parking location, and volume of goods delivered (or picked up) were identified as significant features influencing vehicle dwell time, corroborating with findings in the literature.